Nextflow Registry | nf-core/deeptmhmm@0.0.0-be790aa

nf-core/deeptmhmm @ 0.0.0-be790aa

A Deep Learning Model for Transmembrane Topology Prediction and Classification

transmembrane protein classification

Latest version: 0.0.0-be790aa

Total downloads: 14

Source: nf-core/modules

Authors: @vagkaratzas

Maintainers: @vagkaratzas

A Deep Learning Model for Transmembrane Topology Prediction and Classification

Add the following snippet to your workflow script to include this module.

include { DEEPTMHMM } from 'nf-core/deeptmhmm'

MIT License

Process

`Name`	`DEEPTMHMM`

Input 1 channel

#1 tuple

`meta` map	Groovy Map containing sample information e.g. [ id:'test', single_end:false ]
`fasta` file	Database of sequences in FASTA format `*.{fasta,fa,fasta.gz,fa.gz}`

Output 7 channels

#1 md tuple

`meta` map	Groovy Map containing sample information e.g. [ id:'test', single_end:false ]
`biolib_results/deeptmhmm_results.md` file	Markdown results file `biolib_results/deeptmhmm_results.md`

#2 csv tuple

`meta` map	Groovy Map containing sample information e.g. [ id:'test', single_end:false ]
`biolib_results/*_probs.csv` file	CSV file with per-residue predictions for the likelihood of each amino acid being in structural regions such as Beta-sheet, Periplasm, Membrane, Inside, Outside or Signal (only when querying against genomic fasta) `biolib_results/*_probs.csv`

#3 png tuple

`meta` map	Groovy Map containing sample information e.g. [ id:'test', single_end:false ]
`biolib_results/plot.png` file	Most likely topology probability line plots (only when querying against genomic fasta) `biolib_results/plot.png`

#4 gff3 tuple

`meta` map	Groovy Map containing sample information e.g. [ id:'test', single_end:false ]
`biolib_results/TMRs.gff3` file	Predicted topologies (inside, outside, TMhelix) in general Feature Format Version 3 `biolib_results/TMRs.gff3`

#5 line3 tuple

`meta` map	Groovy Map containing sample information e.g. [ id:'test', single_end:false ]
`biolib_results/predicted_topologies.3line` file	Predicted topologies and information of protein sequences in three lines (name, sequence, topology) `biolib_results/predicted_topologies.3line`

#6 versions_biolib tuple

`${task.process}` string	The name of the process
`biolib` string	The name of the tool
`biolib --version 2>&1 \| sed 's/.*version //'` eval	The expression to obtain the version of the tool

#7 versions_python tuple

`${task.process}` string	The name of the process
`python` string	The name of the tool
`python --version \| sed 's/Python //'` eval	The expression to obtain the version of the tool

Tool	Description	Homepage
deeptmhmm	Deep Learning model for Transmembrane Helices protein domain prediction through the BioLib Python Client	https://dtu.biolib.com/DeepTMHMM

Version	0.0.0-be790aa
Commit ID	be790aafaa178554b403f862164eb27042f0b35a
Release Date	24 Apr 2026 15:00:44 (UTC)
Download URL	https://registry.nextflow.io/api/v1/modules/nf-core%2Fdeeptmhmm/0.0.0-be790aa/download
OCI Store URL	https://public.cr.seqera.io/v2/nextflow/plugin/modules/nf-core/deeptmhmm/blobs/sha256:b20743f692a1a9787dae8fc9a94b915e30811c47bd51bf4f28b4527434314b5a
Size	3.1 KB
Checksum	sha256:b20743f692a1a9787dae8fc9a94b915e30811c47bd51bf4f28b4527434314b5a